多目标约束下的电力网络负荷优化分配研究
Research on Optimal Load Allocation in Power Network Under Multi-Objective Constraints
罗宗杰 1郑世明 1钟俊琛1
作者信息
摘要
当电力网络某些区域负荷过重时,会导致电压下降、频率不稳定,进而影响电子设备的正常工作和供电质量.合理优化分配电力网络的负荷,是典型的多目标优化问题.对此,提出电力网络负荷多目标优化分配方法.以负荷平衡与功率总和作为约束条件,建立多目标下的电力网络负荷优化分配模型.引入非劣分层理论改进差分粒子群混合算法,求解负荷优化分配模型,以获取负荷优化分配解集.利用基点和熵的多指标评价方法,在负荷优化分配解集中确定唯一解,以完成电力网络负荷优化分配.试验结果表明,该方法可有效优化分配电网中的负荷,减少供电煤耗、污染物排放量与调度机组数量,缩短负荷调整时间,提升电力网络发电机组出力值,且具有较高的负荷优化性能.该研究对智能电网和智能优化领域具有借鉴意义,能使电力网络运行和管理更加高效和智能化.
Abstract
When certain areas of the power network are overloaded,it will lead to voltage drop and frequency instability,which in turn affects the normal work of electronic equipment and power supply quality.Reasonable and optimal allocation of loads in power network is a typical multi-objective optimization problem.In this regard,a multi-objective optimal allocation method of power network load is proposed.Taking load balance and power sum as the constraints,the optimal allocation model of power network load under multi-objective is established.Non-inferiority stratification theory is introduced to improve the differential particle swarm hybrid algorithm to solve the load optimization allocation model and obtain the solution set of load optimization allocation.Using the base point and entropy multi-indicator evaluation method,the unique solution is determined in the load optimization allocation solution set to complete the load optimization allocation of electric power network.The experimental results show that the method can effectively optimize the distribution of loads in the power grid,reduce the coal consumption of power supply,pollutant emissions and the number of units to be dispatched,shorten the load adjustment time,improve the output value of generating units in the power network,and have a high performance of load optimization.The research is of great significance in the field of smart grid and smart optimization,which can lead to more efficient and intelligent of power network operation and management.
关键词
电力网络/多目标优化/差分粒子群混合算法/非劣分层理论/多指标评价/节能减排/负荷分配Key words
Power network/Multi-objective optimization/Differential particle swarm hybrid algorithm/Non-inferiority stratification theory/Multi-indicator evaluation/Energy saving and emission reduction/Load distribution引用本文复制引用
出版年
2024
NETL
NSTL
万方数据